Precise alignment of images is important in a number of endeavours, including the calibration of colour registration in printing processes and mask alignment during manufacture of electronic circuits and circuit boards. One method of achieving alignment is to include one or more alignment marks on the target substrate and exploit the properties of these marks to determine pre-defined locations in an image. Examples of alignment marks include dots, lines, and/or cross patterns.
One method for detecting patterns, such as alignment marks, is cross correlation. For linear systems, correlation, or matched filtering, can be shown to be mathematically an optimal detection method. Correlation in two dimensions is not generally invariant with orientation or scaling. An existing method of generating orientation and scale invariant alignment marks provides limited accuracy due to the frequency response of the alignment marks, where energy is concentrated at low frequencies.